import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import plotly.express as px

try:
    df=pd.read_excel("financial_loan.xlsx")
except FileNotFoundError as e:
    print(e)

df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 38576 entries, 0 to 38575
Data columns (total 24 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   id                     38576 non-null  int64         
 1   address_state          38576 non-null  object        
 2   application_type       38576 non-null  object        
 3   emp_length             38576 non-null  object        
 4   emp_title              37138 non-null  object        
 5   grade                  38576 non-null  object        
 6   home_ownership         38576 non-null  object        
 7   issue_date             38576 non-null  datetime64[ns]
 8   last_credit_pull_date  38576 non-null  datetime64[ns]
 9   last_payment_date      38576 non-null  datetime64[ns]
 10  loan_status            38576 non-null  object        
 11  next_payment_date      38576 non-null  datetime64[ns]
 12  member_id              38576 non-null  int64         
 13  purpose                38576 non-null  object        
 14  sub_grade              38576 non-null  object        
 15  term                   38576 non-null  object        
 16  verification_status    38576 non-null  object        
 17  annual_income          38576 non-null  float64       
 18  dti                    38576 non-null  float64       
 19  installment            38576 non-null  float64       
 20  int_rate               38576 non-null  float64       
 21  loan_amount            38576 non-null  int64         
 22  total_acc              38576 non-null  int64         
 23  total_payment          38576 non-null  int64         
dtypes: datetime64[ns](4), float64(4), int64(5), object(11)
memory usage: 7.1+ MB

df[df.duplicated()]

df.isnull().sum()

id                          0
address_state               0
application_type            0
emp_length                  0
emp_title                1438
grade                       0
home_ownership              0
issue_date                  0
last_credit_pull_date       0
last_payment_date           0
loan_status                 0
next_payment_date           0
member_id                   0
purpose                     0
sub_grade                   0
term                        0
verification_status         0
annual_income               0
dti                         0
installment                 0
int_rate                    0
loan_amount                 0
total_acc                   0
total_payment               0
dtype: int64

catcols=df.select_dtypes(include=['object','category']).columns.tolist()
for i in catcols:
    print(df[i].value_counts())
    print('--------------------------------------------')

address_state
CA    6894
NY    3701
FL    2773
TX    2664
NJ    1822
IL    1486
PA    1482
VA    1375
GA    1355
MA    1310
OH    1188
MD    1027
AZ     833
WA     805
CO     770
NC     759
CT     730
MI     685
MO     660
MN     592
NV     482
SC     464
WI     446
OR     436
AL     432
LA     426
KY     320
OK     293
KS     260
UT     252
AR     236
DC     214
RI     196
NM     183
HI     170
WV     167
NH     161
DE     110
MT      79
WY      79
AK      78
SD      63
VT      54
MS      19
TN      17
IN       9
ID       6
NE       5
IA       5
ME       3
Name: count, dtype: int64
--------------------------------------------
application_type
INDIVIDUAL    38576
Name: count, dtype: int64
--------------------------------------------
emp_length
10+ years    8870
< 1 year     4575
2 years      4382
3 years      4088
4 years      3428
5 years      3273
1 year       3229
6 years      2228
7 years      1772
8 years      1476
9 years      1255
Name: count, dtype: int64
--------------------------------------------
emp_title
US Army                             135
Bank of America                     109
IBM                                  67
AT&T                                 63
Wells Fargo                          57
                                   ... 
CH Newton Builders, Inc               1
NGS Associates, Inc.                  1
Ridgecrest Capital Partners           1
rice &werthmann                       1
Five County Assoc of Governments      1
Name: count, Length: 28525, dtype: int64
--------------------------------------------
grade
B    11674
A     9689
C     7904
D     5182
E     2786
F     1028
G      313
Name: count, dtype: int64
--------------------------------------------
home_ownership
RENT        18439
MORTGAGE    17198
OWN          2838
OTHER          98
NONE            3
Name: count, dtype: int64
--------------------------------------------
loan_status
Fully Paid     32145
Charged Off     5333
Current         1098
Name: count, dtype: int64
--------------------------------------------
purpose
Debt consolidation    18214
credit card            4998
other                  3824
home improvement       2876
major purchase         2110
small business         1776
car                    1497
wedding                 928
medical                 667
moving                  559
house                   366
vacation                352
educational             315
renewable_energy         94
Name: count, dtype: int64
--------------------------------------------
sub_grade
B3    2834
A4    2803
A5    2654
B5    2644
B4    2455
C1    2089
B2    1990
C2    1972
B1    1751
A3    1740
C3    1490
A2    1440
D2    1314
C4    1202
C5    1151
D3    1144
A1    1052
D4     960
D1     913
D5     851
E1     750
E2     640
E3     538
E4     448
E5     410
F1     325
F2     243
F3     182
F4     163
F5     115
G1     101
G2      78
G4      56
G3      48
G5      30
Name: count, dtype: int64
--------------------------------------------
term
36 months    28237
60 months    10339
Name: count, dtype: int64
--------------------------------------------
verification_status
Not Verified       16464
Verified           12335
Source Verified     9777
Name: count, dtype: int64
--------------------------------------------

print('Total Loan Applications:',df['id'].count())

Total Loan Applications: 38576

df['IDYM']=df['issue_date'].dt.strftime('%Y-%m')
lma=df[df['issue_date'].dt.to_period('M')==df['IDYM'].max()].shape[0]
print(f'Latest month Applications ({(df['IDYM'].max())}): {lma}')

Latest month Applications (2021-12): 4314

mom=df['IDYM'].value_counts().reset_index(name='TA').sort_values(by='IDYM')

plt.figure(figsize=(15,5))
plt.fill_between(mom['IDYM'],mom['TA'],color='skyblue',alpha=0.5)
plt.plot(mom['IDYM'],mom['TA'],marker='.',color='blue')

for x,y in zip(mom['IDYM'],mom['TA']):
    plt.text(x,y+50,str(y),ha='center',va='bottom')

plt.title('No. of Loan Applications per Month')
plt.xlabel('Year-Months')
plt.ylabel('Loan Applications')
plt.ylim(2000,4500)
plt.tight_layout()
plt.grid(linestyle='--',alpha=0.6)
plt.show()

print(f'Total Funded Amount: ${(df['loan_amount'].sum()/1000000).round(2)} M')

Total Funded Amount: $435.76 M

mfi=df.groupby('IDYM')['loan_amount'].sum().reset_index(name='TLA').sort_values(by='IDYM')
mfi

plt.figure(figsize=(15,5))
mfi['TLAM']=round(mfi['TLA']/1000000,2)
plt.fill_between(mfi['IDYM'],mfi['TLAM'],color='tomato',alpha=0.3)
plt.plot(mfi['IDYM'],mfi['TLAM'],marker='.',color='orangered')

for x,y in zip(mfi['IDYM'],mfi['TLAM']):
    plt.text(x,y+1,'$'+str(y)+'M',ha='center',va='bottom')

plt.title('Total Amount Funded per Month')
plt.xlabel('Year-Months')
plt.ylabel('Amount in Millions')
plt.ylim(20,60)
plt.grid(linestyle='--',alpha=0.6)
plt.show()

print(f'Total Amount Received: ${round(df['total_payment'].sum()/1000000,2)}M')

Total Amount Received: $473.07M

print(f'Average Interest Rate: {(df['int_rate'].mean()*100).round(2)}%')

Average Interest Rate: 12.05%

#### Q: Average DTI (Debt-to-Income) Ratio:
print(f'Average DTI Ratio: {(df['dti'].mean()*100).round(2)}%')

Average DTI Ratio: 13.33%

gl=df[df['loan_status'].isin(['Fully Paid','Current'])]
print(f'No. of GoodLoans: {gl['id'].count()}')
print(f'Funded Amt for GoodLoans: ${round(gl['loan_amount'].sum()/1000000,2)} M')
print(f'GoodLoans Amt Recieved: ${round(gl['total_payment'].sum()/1000000,2)} M')
print(f'GoodLoans Applications: {round(gl['id'].count()/df['id'].count()*100)}%')

No. of GoodLoans: 33243
Funded Amt for GoodLoans: $370.22 M
GoodLoans Amt Recieved: $435.79 M
GoodLoans Applications: 86%

bl=df[df['loan_status']=='Charged Off']
print(f'No. of BadLoans: {bl['id'].count()}')
print(f'Funded Amt for BadLoans: ${round(bl['loan_amount'].sum()/1000000,2)} M')
print(f'Loss Due to BadLoans: ${round(bl['total_payment'].sum()/1000000,2)} M')
print(f'Percentage of Loss: {round(bl['total_payment'].sum()/df['loan_amount'].sum()*100,2)}%')
print(f'BadLoans Applications: {round(bl['id'].count()/df['id'].count()*100)}%')

No. of BadLoans: 5333
Funded Amt for BadLoans: $65.53 M
Loss Due to BadLoans: $37.28 M
Percentage of Loss: 8.56%
BadLoans Applications: 14%

rta=df.groupby('address_state')['id'].count().reset_index(name='TLA').sort_values(by='TLA',ascending=False)

plt.figure(figsize=(15,10))
bar=plt.barh(rta['address_state'],rta['TLA'],color='skyblue',height=0.6)
plt.gca().invert_yaxis()
plt.title('Total Loan Applications per State')
plt.ylabel('State',fontsize=12)
plt.xlabel('Number of Loan Applications',fontsize=12)

plt.bar_label(bar,
              labels=[f"{v:,}" for v in rta['TLA']],
              fontsize=10,
              padding=5)

plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

rfa=df.groupby('address_state')['loan_amount'].sum().reset_index(name='TFA').sort_values(by='TFA',ascending=False)

plt.figure(figsize=(15,10))
bar=plt.barh(rfa['address_state'],rfa['TFA']//1000000,color='lightcoral',height=0.6)
plt.gca().invert_yaxis()
plt.title('Total Fund Allocations per State ($)')
plt.ylabel('State',fontsize=12)
plt.xlabel('Amount in Millions',fontsize=12)

plt.bar_label(bar,
              labels=[f"{v}K" for v in (rfa['TFA']//1000)],
              fontsize=10,
              padding=5)

plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

rar=df.groupby('address_state')['total_payment'].sum().reset_index(name='TAR').sort_values(by='TAR',ascending=False)

plt.figure(figsize=(15,10))
bar=plt.barh(rar['address_state'],rar['TAR']//1000000,color='green',height=0.6,alpha=0.7)
plt.gca().invert_yaxis()
plt.title('Total Amount Received per State ($)')
plt.ylabel('State',fontsize=12)
plt.xlabel('Amount in Millions',fontsize=12)

plt.bar_label(bar,
              labels=[f"{v}K" for v in (rar['TAR']//1000)],
              fontsize=10,
              padding=5)

plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

lat=df.groupby('term')['id'].count()
plt.figure(figsize=(5,5))
plt.pie(lat,labels=lat.index,autopct=lambda p: f'{p:.0f}%\n({int(p*lat.sum()/100)})',colors=['darkcyan','mediumturquoise'],startangle=90)
plt.title('Number of Loan Applications by Tenure')
plt.show()

fat=df.groupby('term')['loan_amount'].sum()
plt.figure(figsize=(5,5))
plt.pie(fat,labels=fat.index,autopct=lambda p: f'{p:.0f}%\n(${p*fat.sum()/100/1e6:.1f}M)',colors=['orangered','coral'],startangle=90)
plt.title('Total Fund Allocated by Tenure')
plt.show()

art=df.groupby('term')['total_payment'].sum()
plt.figure(figsize=(5,5))
plt.pie(art,labels=fat.index,autopct=lambda p: f'{p:.0f}%\n(${p*fat.sum()/100/1e6:.1f}M)',colors=['olivedrab','greenyellow'],startangle=90)
plt.title('Total Amount Received by Tenure')
plt.show()

df['cov']=np.where(df['loan_status']=='Charged Off',1,0)

clt=df.groupby('term')['cov'].sum()

plt.figure(figsize=(5,5))
plt.pie(clt,labels=clt.index,startangle=90,colors=['darkred','tomato'],autopct=lambda p: f'{p:.0f}%\n({int(p*clt.sum()/100)})')
plt.title('Number of Loans Defaulted by Tenure')
plt.show()

ela=df.groupby('emp_length')['id'].count().reset_index(name='TA').sort_values(by='TA',ascending=True)

plt.figure(figsize=(15,5))
bar=plt.barh(ela['emp_length'],ela['TA'],color=plt.cm.Blues(ela['TA']/ela['TA'].max()))
plt.title("Number of Loan Applications by Client's Work Experience")
plt.xlabel("Number of Applications")
plt.ylabel("Work Experience")
plt.bar_label(bar,padding=5)
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

cee=df.groupby('emp_length').agg(TLA=('id','count'),NLD=('cov','sum')).reset_index()
cee['Percent']=round(cee['NLD']/cee['TLA']*100)
cee = cee.sort_values('Percent', ascending=True)


plt.figure(figsize=(20,6))
bars = plt.barh(
    cee['emp_length'],
    cee['Percent'],
    color=plt.cm.Reds(cee['Percent'] / cee['Percent'].max())
)

plt.title("Number of Loans Defaulted &  Percentage by Client's Work Experience")
plt.xlabel("Default Rate (%)")
plt.ylabel("Work Experience")
plt.xlim(0,20)

for bar, nld, pct in zip(bars, cee['NLD'], cee['Percent']):
    plt.text(
        bar.get_width()+2.5,
        bar.get_y() + bar.get_height() / 2,
        f"Defaulted: {nld} | {pct:.0f}%",
        va='center',
        ha='right',
        fontsize=10,
    )

plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.subplots_adjust(left=0.3, right=0.95)
plt.show()

tap=df.groupby('purpose').agg(TotalLoans=('id','count')).reset_index().sort_values(by='TotalLoans',ascending=True)

plt.figure(figsize=(15,5))
bar=plt.barh(tap['purpose'],tap['TotalLoans'],color=plt.cm.viridis(tap['TotalLoans']/tap['TotalLoans'].max()))
plt.title("Number of Loan Applications by Purpose")
plt.xlabel("Number of Loan Applications")
plt.ylabel("Purposes")
plt.bar_label(bar,padding=5)
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

taf=df.groupby('purpose').agg(FundedAmt=('loan_amount','sum')).reset_index().sort_values(by='FundedAmt',ascending=True)

plt.figure(figsize=(15,5))
bar=plt.barh(taf['purpose'],taf['FundedAmt']//1000,color=plt.cm.Wistia(taf['FundedAmt']/taf['FundedAmt'].max()))
plt.title("Total Amount Funded by Purpose ($)")
plt.xlabel(" Amount Funded")
plt.ylabel("Purposes")
plt.bar_label(bar,labels=[f'{v}k' for v in taf['FundedAmt']//1000],padding=5)
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

ldp=df.groupby('purpose').agg(TLA=('id','count'),NLD=('cov','sum')).reset_index()
ldp['Percent']=round(ldp['NLD']/ldp['TLA']*100)
ldp = ldp.sort_values('Percent', ascending=True)

plt.figure(figsize=(15,5))
colors=plt.cm.autumn(np.linspace(1,0,ldp.shape[0]))
bar=plt.barh(ldp['purpose'],ldp['Percent'],color=colors)
plt.title("Number of Loan Defaults & Percentage by Purpose of Loan Required")
plt.xlabel("Number of Loan Defaults")
plt.ylabel("Purposes")
plt.xlim(0,35)

for bar,x,y in zip(bar,ldp['NLD'],ldp['Percent']):
    plt.text(
        bar.get_width()+4.5,
        bar.get_y()+bar.get_height()/2,
        f'Defaulted: {x} | {y:.0f}%',
        va='center',
        ha='right',
        fontsize=10,
    )
    
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

aho=df.groupby('home_ownership')['id'].count().reset_index(name='TA')

plt.figure(figsize=(15,8))
fig=px.treemap(
    aho,
    path=['home_ownership'],
    color='TA',
    values='TA',
    color_continuous_scale='Blues',
    title='Number of Loan Applications by Home Ownership'
)
fig.update_traces(
    textinfo='label+value',
    textfont_size=12
)

fig.show()

<Figure size 1500x800 with 0 Axes>

aho=df.groupby('home_ownership')['loan_amount'].sum().reset_index(name='TAF')

plt.figure(figsize=(15,8))
fig=px.treemap(
    aho,
    path=['home_ownership'],
    color='TAF',
    values='TAF',
    color_continuous_scale='Oranges',
    title='Total Amount Funded by Home Ownership'
)
fig.update_traces(
    textinfo='label+value',
    textfont_size=12
)

fig.show()

<Figure size 1500x800 with 0 Axes>

ldho=df.groupby('home_ownership').agg(TLA=('id','count'),NLD=('cov','sum')).reset_index()
ldho['Percent']=round(ldho['NLD']/ldho['TLA']*100)
ldho=ldho.query("Percent>0")

plt.figure(figsize=(15,8))
fig=px.treemap(
    ldho,
    path=['home_ownership'],
    color='Percent',
    values='Percent',
    custom_data=['TLA', 'NLD', 'Percent'],
    color_continuous_scale='Reds',
    title='Loans Defaulted by Home Ownership'
)
fig.update_traces(
    texttemplate=
            "%{label}<br><br>"
            "Total Applications: %{customdata[0]}<br>"
            "Loans Defaulted: %{customdata[1]}<br>"
            "Percentage: %{customdata[2]}%<br>",
    textfont_size=12
)

fig.show()

<Figure size 1500x800 with 0 Axes>

lds=df.groupby('verification_status')['cov'].sum().reset_index(name='NLD').sort_values(by='NLD',ascending=True)

plt.figure(figsize=(12,3))
bar=plt.barh(lds['verification_status'],lds['NLD'],height=0.4,color='purple')
plt.title("Number of Loans Defaulted by Sources")
plt.xlabel("Number of Loans Defaulted")
plt.ylabel("Verification Status")
plt.bar_label(bar,padding=5)
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

lts = df.groupby('loan_status')['id'].count()

plt.figure(figsize=(5,5))

colors = ['#2E7D32', '#C62828', '#F9A825']
explode = [0.08,0,0]

plt.pie(
    lts,
    labels=lts.index,
    autopct=lambda p: f'{p:.0f}%\n({int(p*lts.sum()/100):,})',
    colors=colors,
    explode=explode,
    startangle=90,
    counterclock=False,
    pctdistance=0.80,       
    labeldistance=1.05,
    wedgeprops=dict(width=0.4, edgecolor='white')
)

plt.text(0, 0, "LOANS\nSTATUS", ha='center', va='center',
         fontsize=14, fontweight='bold')

plt.title("Overview of Bank's Loans", fontsize=13, fontweight='bold')

plt.tight_layout()
plt.show()

sld=df.groupby('address_state',as_index=False).agg(TC=('id','count'),TLD=('cov','sum'))
sld['Percent%']=round(sld['TLD']/sld['TC']*100)

sld_sorted = sld.sort_values(by='Percent%', ascending=False)

colors = plt.cm.Reds(
    sld_sorted['Percent%'] / sld_sorted['Percent%'].max()
)

plt.figure(figsize=(12,10))

bars = plt.barh(
    sld_sorted['address_state'],
    sld_sorted['Percent%'],
    color=colors,
    height=0.6
)

plt.gca().invert_yaxis()
plt.title('Volume & Rate of Defaulted Loans per State', fontsize=12, fontweight='bold')
plt.xlabel('Defaulted Rate (%)', fontsize=12)
plt.ylabel('State', fontsize=12)
plt.xlim(0,100)

for bar, d, p in zip(bars, sld_sorted['TLD'], sld_sorted['Percent%']):
    plt.text(
        bar.get_width()+1,
        bar.get_y() + bar.get_height()/2,
        f'Defaults: {d} | {p:.0f}%',
        va='center',
        fontsize=9
    )

plt.grid(axis='x', linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()

tald=df.groupby('total_acc')['cov'].sum().reset_index(name='TLD')
tald['Ratio']=round(tald['TLD']/df['cov'].sum()*100,2)
tald.query("Ratio>1")

tald['total_acc'].corr(tald['Ratio'])

np.float64(-0.8117562018798052)

# There is no relation between total no. of accounts and loan defaults.

def ic(val):
    if val<=50000:
        return 'Lower Class'
    elif val>50000 and val<=100000:
        return 'Lower Middle Class'
    elif val>100000 and val<=500000:
        return 'Middle Class'
    elif val>500000 and val<=2500000:
        return 'Upper Middle Class'
    else:
        return 'Super Class'
df['IncCat']=df['annual_income'].apply(ic)

taic=df.groupby('IncCat')['id'].count().reset_index(name='TLA').sort_values(by='TLA',ascending=True)

plt.figure(figsize=(15,5))
bar=plt.barh(taic['IncCat'],taic['TLA'],color=plt.cm.Blues(taic['TLA']/taic['TLA'].max()))
plt.title("Number of Loan Applications by Income Category")
plt.xlabel("Number of Applications")
plt.ylabel("Income Category")
plt.xlim(0,25000)
plt.bar_label(bar,padding=5)
plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

ldic=df.groupby('IncCat')['cov'].sum().astype(int).reset_index(name='TLD').sort_values(by='TLD')
ldic=pd.merge(ldic,taic,on='IncCat',how='left')
ldic['DefaultRate']=(ldic['TLD']/ldic['TLA']*100).astype(int)
ldic=ldic.sort_values(by='DefaultRate')

plt.figure(figsize=(20,6))
bars = plt.barh(
    ldic['IncCat'],
    ldic['DefaultRate'],
    color=plt.cm.Reds(ldic['DefaultRate'] / ldic['DefaultRate'].max())
)

plt.title("Number of Loans Defaulted &  Percentage by Income Category")
plt.xlabel("Default Rate (%)")
plt.ylabel("Income Category")
plt.xlim(0,20)

for bar, nld, pct in zip(bars, ldic['TLD'], ldic['DefaultRate']):
    plt.text(
        bar.get_width()+2.5,
        bar.get_y() + bar.get_height() / 2,
        f"Defaulted: {nld} | {pct:.0f}%",
        va='center',
        ha='right',
        fontsize=10,
    )

plt.grid(axis='x', linestyle='--', alpha=0.6)
plt.subplots_adjust(left=0.3, right=0.95)
plt.show()

	IDYM	TLA
0	2021-01	25031650
1	2021-02	24647825
2	2021-03	28875700
3	2021-04	29800800
4	2021-05	31738350
5	2021-06	34161475
6	2021-07	35813900
7	2021-08	38149600
8	2021-09	40907725
9	2021-10	44893800
10	2021-11	47754825
11	2021-12	53981425

Understanding & Data Cleaning¶

Exploratory Data Analysis (EDA)¶

Q: Total Loan Applications¶

Q: MTD (Month-to-Date) Total Loan Applications¶

Q: MOM (Month-on-Month) Loan Applications¶

Q: Total Funded Amount¶

Q: MOM Total Funded Amount¶

Q: Total Amount Received¶

Q: Average Interest Rate¶

Q: Good Loan Matrix¶

Q: Bad Loan Matrix¶

Q: Regional Analysis by State for Total Loan Applications¶

Q: Regional Analysis by State for Total Fund Allocation¶

Q: Regional Analysis by State for Total Amount Received¶

Q: Total Loan Application by Term¶

Q: Total Funded Amount by Term¶

Q: Total Amount Received by Term¶

Q: Total Loan Applications by Employee Len¶

Q: Loan Defaults as per Employee Len¶

Q: Total Loan Applications by purpose¶

Q: Total Amount Funded by purpose¶

Q: Loans Defaults by Purpose¶

Q: Loan Applications by Home Ownership¶

Q: Total Amount Funded by Home Ownership¶

Q: No. of Loan Defaults by Home Ownership¶

Q: No. of Loans Default due to sources¶

Q: Overview of Loan Applications¶

Q: State wise Loan Defaults.¶

Q: Loan Defaults by total_acc¶

Q: Loan Applications by Income category¶

Q: Loan defaulted by Income Category¶

	id	address_state	application_type	emp_length	emp_title	grade	home_ownership	issue_date	last_credit_pull_date	last_payment_date	...	sub_grade	term	verification_status	annual_income	dti	installment	int_rate	loan_amount	total_acc	total_payment
0	1077430	GA	INDIVIDUAL	< 1 year	Ryder	C	RENT	2021-02-11	2021-09-13	2021-04-13	...	C4	60 months	Source Verified	30000.0	0.0100	59.83	0.1527	2500	4	1009
1	1072053	CA	INDIVIDUAL	9 years	MKC Accounting	E	RENT	2021-01-01	2021-12-14	2021-01-15	...	E1	36 months	Source Verified	48000.0	0.0535	109.43	0.1864	3000	4	3939
2	1069243	CA	INDIVIDUAL	4 years	Chemat Technology Inc	C	RENT	2021-01-05	2021-12-12	2021-01-09	...	C5	36 months	Not Verified	50000.0	0.2088	421.65	0.1596	12000	11	3522
3	1041756	TX	INDIVIDUAL	< 1 year	barnes distribution	B	MORTGAGE	2021-02-25	2021-12-12	2021-03-12	...	B2	60 months	Source Verified	42000.0	0.0540	97.06	0.1065	4500	9	4911
4	1068350	IL	INDIVIDUAL	10+ years	J&J Steel Inc	A	MORTGAGE	2021-01-01	2021-12-14	2021-01-15	...	A1	36 months	Verified	83000.0	0.0231	106.53	0.0603	3500	28	3835
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38571	803452	NJ	INDIVIDUAL	< 1 year	Joseph M Sanzari Company	C	MORTGAGE	2021-07-11	2021-05-16	2021-05-16	...	C1	60 months	Verified	100000.0	0.1986	551.64	0.1299	24250	33	31946
38572	970377	NY	INDIVIDUAL	8 years	Swat Fame	C	RENT	2021-10-11	2021-04-16	2021-05-16	...	C1	60 months	Verified	50000.0	0.0458	579.72	0.1349	25200	18	31870
38573	875376	CA	INDIVIDUAL	5 years	Anaheim Regional Medical Center	D	RENT	2021-09-11	2021-05-16	2021-05-16	...	D5	60 months	Verified	65000.0	0.1734	627.93	0.1749	25000	20	35721
38574	972997	NY	INDIVIDUAL	5 years	Brooklyn Radiology	D	RENT	2021-10-11	2021-05-16	2021-05-16	...	D5	60 months	Verified	368000.0	0.0009	612.72	0.1825	24000	9	33677
38575	682952	NY	INDIVIDUAL	4 years	Allen Edmonds	F	RENT	2021-07-11	2021-05-16	2021-05-16	...	F3	60 months	Verified	80000.0	0.0600	486.86	0.2099	18000	7	27679

	total_acc	TLD	Ratio
2	4	72	1.35
3	5	84	1.58
4	6	98	1.84
5	7	123	2.31
6	8	162	3.04
7	9	152	2.85
8	10	170	3.19
9	11	184	3.45
10	12	186	3.49
11	13	181	3.39
12	14	217	4.07
13	15	187	3.51
14	16	216	4.05
15	17	194	3.64
16	18	174	3.26
17	19	189	3.54
18	20	179	3.36
19	21	169	3.17
20	22	149	2.79
21	23	160	3.00
22	24	175	3.28
23	25	140	2.63
24	26	113	2.12
25	27	131	2.46
26	28	135	2.53
27	29	115	2.16
28	30	125	2.34
29	31	99	1.86
30	32	82	1.54
31	33	107	2.01
32	34	71	1.33
33	35	82	1.54
34	36	82	1.54
36	38	67	1.26
37	39	62	1.16